Radiation oncology and nuclear medicine software provider DOSIsoft has received U.S. Food and Drug Administration (FDA) 510(k) clearance for its ThinkQA-EPIbeam modular software suite and has also launched an updated version of its Planet Dose dosimetry system for molecular radiotherapy.
ThinkQA-EPIbeam is composed of an electronic portal imaging (EPI) beam module for patient-specific quality assurance (QA) for automated pretreatment verification of irradiation beams.
The quality-control EPIbeam tool for external-beam radiation therapy is based on an EPI device integrated into the ThinkQA software suite, a web-based system for radiation therapy QA and error detection. It is dedicated to verifying radiation therapy beams prior to patient treatments by comparing the delivered dose, reconstructed from electronic portal images, with the planned dose computed by the treatment planning system. It can be used to detect errors in the delivery of radiation therapy before treatment, such as the corruption of the transferred plan data to the treatment unit, inappropriate multileaf collimator sequence, or beam output malfunctioning.
EPIbeam also reduces the time spent on beam controls compared with phantom-based measurement methods and, thus, enables systematic pretreatment controls, according to DOSIsoft. It is compatible with all current treatment techniques: 3D chemoradiation therapy, intensity-modulated radiotherapy, volumetric-modulated arc therapy, and standard or flattening filter free beam fluence modes.
The company has also launched a new version of its Planet Dose multiradionuclide dosimetry system for molecular radiotherapy. Planet Dose has received the CE Mark for 3D personalized dosimetry for yttrium-90 and lutetium-177 therapies.
The advanced features include multiple time point quantification, elastic image registration, automatic deformable propagation of volume of interest across times, residence time calculation, dose computation, and consolidation of multitreatment stages, as well as advanced dose distribution analysis.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
